Closing the Gap from Data Science to Production AI

The path from prototype to production AI gets blocked by a critical challenge: the complex data engineering required to deliver reliable, production-ready data to models for training and inference. 

While data scientists experiment with models, the path to production involves significant engineering work to transform and serve data for AI at scale. The result? Months of delays, inconsistent performance, and mounting technical debt.

Join us on December 4 at 9 AM PST as Sergio Ferragut, Principal Developer Advocate, and Julia Brouillette, Senior Technical Evangelist demonstrate how modern AI teams are breaking down silos between data science and engineering using an "as code" approach to developing, managing, and serving AI context (including features, embeddings, and prompts).

You'll learn how to:

• Use a simple declarative framework to solve the complex AI data transformations you need
• Automate the creation of production-ready AI data pipelines for batch, streaming, and real-time data that work for both traditional ML and GenAI
• Ensure perfect consistency between training and serving data to eliminate skew
• Serve context to production AI systems with sub-second latency at scale
• Establish processes that scale efficiently across multiple AI/ML applications

Whether you're looking to accelerate your journey to production, scale your AI/ML operations more efficiently, or build more sophisticated AI applications, this session will equip you with practical approaches to unlock the full potential of your data for AI.